Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x23a842c0dd8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x23a8437e6a0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_images = tf.placeholder(dtype=tf.float32, shape = [None, image_width, image_height, image_channels],name = 'input_images')
    z_data = tf.placeholder(dtype=tf.float32, shape = [None, z_dim],name = 'z_data')
    l_r = tf.placeholder(dtype = tf.float32, name = 'learning_rate')
    return input_images, z_data, l_r

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.01
    
    with tf.variable_scope('discriminator',reuse=reuse):
        conv_1 = tf.layers.conv2d(images,64,5,strides=2,padding='same')
        conv_1 = tf.maximum(alpha * conv_1, conv_1)
        
        conv_2 = tf.layers.conv2d(conv_1, 128, 5, strides=2, padding='same')
        conv_2 = tf.layers.batch_normalization(conv_2,  training=True)
        conv_2 = tf.maximum(alpha * conv_2, conv_2)
        
        conv_3 = tf.layers.conv2d(conv_2, 256, 5, strides=2, padding='same')
        conv_3 = tf.layers.batch_normalization(conv_3,  training=True)
        conv_3 = tf.maximum(alpha * conv_3, conv_3)

        flat  = tf.reshape(conv_3, (-1, 4*4*256))
        tensor_logits = tf.layers.dense(flat, 1)
        output = tf.sigmoid(tensor_logits)

    return output, tensor_logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [22]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.01
    
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 4*4*256)
        x1 = tf.reshape(x1, (-1, 4, 4, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(x1, alpha*x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(x2, alpha*x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(x3, alpha*x3)

        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [23]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, is_train=True)
    
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_real)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [24]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [25]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [34]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """

    # i used https://github.com/husams/dlnd_face_generation/blob/master/dlnd_face_generation.ipynb as a reference because i got stuck on this step A LOT
    n_samples, width, height, channels = data_shape
    input_real, input_z, learn_rate = model_inputs(width, height, channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    show_every = 50
    print_every = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_images *= 2
                # TODO: Train Model
                steps += 1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})

                if steps % show_every == 0:
                    n_images = 16
                    show_generator_output(sess, n_images, input_z, channels, data_image_mode)

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [35]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.5669... Generator Loss: 9.8955
Epoch 1/2... Discriminator Loss: 2.2628... Generator Loss: 14.9761
Epoch 1/2... Discriminator Loss: 0.1517... Generator Loss: 2.7252
Epoch 1/2... Discriminator Loss: 0.7024... Generator Loss: 1.3082
Epoch 1/2... Discriminator Loss: 0.2094... Generator Loss: 4.2931
Epoch 1/2... Discriminator Loss: 0.2877... Generator Loss: 11.5180
Epoch 1/2... Discriminator Loss: 1.0357... Generator Loss: 0.8669
Epoch 1/2... Discriminator Loss: 1.0112... Generator Loss: 1.2986
Epoch 1/2... Discriminator Loss: 0.4313... Generator Loss: 2.5089
Epoch 1/2... Discriminator Loss: 0.5411... Generator Loss: 3.2803
Epoch 1/2... Discriminator Loss: 0.7698... Generator Loss: 1.3738
Epoch 1/2... Discriminator Loss: 1.4745... Generator Loss: 0.4203
Epoch 1/2... Discriminator Loss: 0.9731... Generator Loss: 0.8312
Epoch 1/2... Discriminator Loss: 0.7691... Generator Loss: 0.9767
Epoch 1/2... Discriminator Loss: 0.8626... Generator Loss: 0.9383
Epoch 1/2... Discriminator Loss: 0.8667... Generator Loss: 1.1434
Epoch 1/2... Discriminator Loss: 1.2380... Generator Loss: 0.5137
Epoch 1/2... Discriminator Loss: 0.7282... Generator Loss: 1.3261
Epoch 1/2... Discriminator Loss: 1.2505... Generator Loss: 2.3202
Epoch 1/2... Discriminator Loss: 0.7980... Generator Loss: 1.0930
Epoch 1/2... Discriminator Loss: 0.8341... Generator Loss: 1.7789
Epoch 1/2... Discriminator Loss: 0.8633... Generator Loss: 0.8802
Epoch 1/2... Discriminator Loss: 0.9739... Generator Loss: 0.8834
Epoch 1/2... Discriminator Loss: 0.7406... Generator Loss: 1.5409
Epoch 1/2... Discriminator Loss: 0.8703... Generator Loss: 1.7066
Epoch 1/2... Discriminator Loss: 0.8163... Generator Loss: 1.4075
Epoch 1/2... Discriminator Loss: 1.0077... Generator Loss: 0.7345
Epoch 1/2... Discriminator Loss: 1.6605... Generator Loss: 3.3228
Epoch 1/2... Discriminator Loss: 1.1976... Generator Loss: 2.2353
Epoch 1/2... Discriminator Loss: 1.1077... Generator Loss: 0.5995
Epoch 1/2... Discriminator Loss: 0.8090... Generator Loss: 0.9879
Epoch 1/2... Discriminator Loss: 1.1269... Generator Loss: 0.6221
Epoch 1/2... Discriminator Loss: 0.8120... Generator Loss: 1.1428
Epoch 1/2... Discriminator Loss: 0.6791... Generator Loss: 1.2268
Epoch 1/2... Discriminator Loss: 0.6865... Generator Loss: 1.5718
Epoch 1/2... Discriminator Loss: 0.7910... Generator Loss: 1.1353
Epoch 1/2... Discriminator Loss: 0.9692... Generator Loss: 0.8272
Epoch 2/2... Discriminator Loss: 0.8933... Generator Loss: 0.8073
Epoch 2/2... Discriminator Loss: 0.8434... Generator Loss: 1.4152
Epoch 2/2... Discriminator Loss: 0.8261... Generator Loss: 1.4953
Epoch 2/2... Discriminator Loss: 0.6492... Generator Loss: 1.9686
Epoch 2/2... Discriminator Loss: 0.9493... Generator Loss: 0.7767
Epoch 2/2... Discriminator Loss: 0.8481... Generator Loss: 1.2509
Epoch 2/2... Discriminator Loss: 0.9661... Generator Loss: 0.7896
Epoch 2/2... Discriminator Loss: 0.8022... Generator Loss: 1.1323
Epoch 2/2... Discriminator Loss: 0.9317... Generator Loss: 1.0448
Epoch 2/2... Discriminator Loss: 0.7305... Generator Loss: 2.6007
Epoch 2/2... Discriminator Loss: 0.7927... Generator Loss: 0.9113
Epoch 2/2... Discriminator Loss: 0.8703... Generator Loss: 2.2669
Epoch 2/2... Discriminator Loss: 0.7185... Generator Loss: 1.2649
Epoch 2/2... Discriminator Loss: 1.2162... Generator Loss: 0.5540
Epoch 2/2... Discriminator Loss: 1.0116... Generator Loss: 1.0961
Epoch 2/2... Discriminator Loss: 1.3586... Generator Loss: 0.4886
Epoch 2/2... Discriminator Loss: 1.5449... Generator Loss: 0.3239
Epoch 2/2... Discriminator Loss: 1.3303... Generator Loss: 0.4199
Epoch 2/2... Discriminator Loss: 0.8676... Generator Loss: 1.6718
Epoch 2/2... Discriminator Loss: 0.7366... Generator Loss: 1.1594
Epoch 2/2... Discriminator Loss: 0.8376... Generator Loss: 1.2677
Epoch 2/2... Discriminator Loss: 0.7546... Generator Loss: 1.2983
Epoch 2/2... Discriminator Loss: 1.0596... Generator Loss: 0.7259
Epoch 2/2... Discriminator Loss: 0.6975... Generator Loss: 1.1469
Epoch 2/2... Discriminator Loss: 0.6861... Generator Loss: 1.0506
Epoch 2/2... Discriminator Loss: 1.0204... Generator Loss: 0.6420
Epoch 2/2... Discriminator Loss: 0.7108... Generator Loss: 1.3505
Epoch 2/2... Discriminator Loss: 1.3687... Generator Loss: 0.5104
Epoch 2/2... Discriminator Loss: 0.8387... Generator Loss: 2.1470
Epoch 2/2... Discriminator Loss: 0.9623... Generator Loss: 2.3849
Epoch 2/2... Discriminator Loss: 1.0543... Generator Loss: 0.6088
Epoch 2/2... Discriminator Loss: 1.0565... Generator Loss: 0.9231
Epoch 2/2... Discriminator Loss: 1.8267... Generator Loss: 4.7310
Epoch 2/2... Discriminator Loss: 0.5781... Generator Loss: 1.5548
Epoch 2/2... Discriminator Loss: 0.9015... Generator Loss: 0.9253
Epoch 2/2... Discriminator Loss: 0.6507... Generator Loss: 1.2309
Epoch 2/2... Discriminator Loss: 0.7134... Generator Loss: 0.9879

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [36]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.0648... Generator Loss: 20.1406
Epoch 1/1... Discriminator Loss: 0.0671... Generator Loss: 6.3457
Epoch 1/1... Discriminator Loss: 0.0254... Generator Loss: 6.6251
Epoch 1/1... Discriminator Loss: 0.5713... Generator Loss: 2.0638
Epoch 1/1... Discriminator Loss: 0.8366... Generator Loss: 4.8959
Epoch 1/1... Discriminator Loss: 0.3039... Generator Loss: 3.5076
Epoch 1/1... Discriminator Loss: 0.8513... Generator Loss: 4.8726
Epoch 1/1... Discriminator Loss: 0.8512... Generator Loss: 1.1773
Epoch 1/1... Discriminator Loss: 0.6304... Generator Loss: 2.4766
Epoch 1/1... Discriminator Loss: 0.8301... Generator Loss: 0.9136
Epoch 1/1... Discriminator Loss: 0.6246... Generator Loss: 2.9160
Epoch 1/1... Discriminator Loss: 0.7655... Generator Loss: 1.0974
Epoch 1/1... Discriminator Loss: 0.6730... Generator Loss: 2.1401
Epoch 1/1... Discriminator Loss: 1.7116... Generator Loss: 4.9646
Epoch 1/1... Discriminator Loss: 0.7393... Generator Loss: 1.7468
Epoch 1/1... Discriminator Loss: 0.3940... Generator Loss: 2.0201
Epoch 1/1... Discriminator Loss: 1.0744... Generator Loss: 0.7071
Epoch 1/1... Discriminator Loss: 0.5829... Generator Loss: 1.1876
Epoch 1/1... Discriminator Loss: 0.6109... Generator Loss: 1.2991
Epoch 1/1... Discriminator Loss: 0.7687... Generator Loss: 1.5837
Epoch 1/1... Discriminator Loss: 0.7615... Generator Loss: 1.3158
Epoch 1/1... Discriminator Loss: 0.6446... Generator Loss: 1.0700
Epoch 1/1... Discriminator Loss: 0.6513... Generator Loss: 1.1893
Epoch 1/1... Discriminator Loss: 0.4211... Generator Loss: 1.9647
Epoch 1/1... Discriminator Loss: 1.5756... Generator Loss: 0.4908
Epoch 1/1... Discriminator Loss: 0.7154... Generator Loss: 1.9936
Epoch 1/1... Discriminator Loss: 0.6307... Generator Loss: 2.1197
Epoch 1/1... Discriminator Loss: 0.7388... Generator Loss: 1.5214
Epoch 1/1... Discriminator Loss: 0.9916... Generator Loss: 0.6323
Epoch 1/1... Discriminator Loss: 0.5905... Generator Loss: 1.3134
Epoch 1/1... Discriminator Loss: 0.6292... Generator Loss: 1.5781
Epoch 1/1... Discriminator Loss: 0.5510... Generator Loss: 1.2983
Epoch 1/1... Discriminator Loss: 1.7436... Generator Loss: 3.4145
Epoch 1/1... Discriminator Loss: 0.1820... Generator Loss: 2.7333
Epoch 1/1... Discriminator Loss: 0.8825... Generator Loss: 0.8224
Epoch 1/1... Discriminator Loss: 0.7517... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 0.4629... Generator Loss: 1.9630
Epoch 1/1... Discriminator Loss: 0.6741... Generator Loss: 1.8444
Epoch 1/1... Discriminator Loss: 0.5988... Generator Loss: 1.6389
Epoch 1/1... Discriminator Loss: 1.5092... Generator Loss: 0.3767
Epoch 1/1... Discriminator Loss: 0.5613... Generator Loss: 1.5958
Epoch 1/1... Discriminator Loss: 1.0398... Generator Loss: 0.9526
Epoch 1/1... Discriminator Loss: 0.7450... Generator Loss: 1.5547
Epoch 1/1... Discriminator Loss: 0.7018... Generator Loss: 1.3000
Epoch 1/1... Discriminator Loss: 0.7975... Generator Loss: 1.4619
Epoch 1/1... Discriminator Loss: 0.9334... Generator Loss: 2.8281
Epoch 1/1... Discriminator Loss: 0.8201... Generator Loss: 0.9075
Epoch 1/1... Discriminator Loss: 0.9238... Generator Loss: 0.7812
Epoch 1/1... Discriminator Loss: 0.7671... Generator Loss: 1.1863
Epoch 1/1... Discriminator Loss: 0.7409... Generator Loss: 2.0368
Epoch 1/1... Discriminator Loss: 1.4896... Generator Loss: 0.3760
Epoch 1/1... Discriminator Loss: 0.8271... Generator Loss: 2.4770
Epoch 1/1... Discriminator Loss: 1.8102... Generator Loss: 3.6647
Epoch 1/1... Discriminator Loss: 0.7858... Generator Loss: 1.5229
Epoch 1/1... Discriminator Loss: 0.9765... Generator Loss: 2.0319
Epoch 1/1... Discriminator Loss: 1.0862... Generator Loss: 0.6384
Epoch 1/1... Discriminator Loss: 1.3146... Generator Loss: 0.4427
Epoch 1/1... Discriminator Loss: 1.1089... Generator Loss: 1.8513
Epoch 1/1... Discriminator Loss: 1.2295... Generator Loss: 0.5857
Epoch 1/1... Discriminator Loss: 0.8811... Generator Loss: 0.7723
Epoch 1/1... Discriminator Loss: 1.0998... Generator Loss: 2.6564
Epoch 1/1... Discriminator Loss: 0.8480... Generator Loss: 0.9381
Epoch 1/1... Discriminator Loss: 1.0004... Generator Loss: 2.3617
Epoch 1/1... Discriminator Loss: 0.8219... Generator Loss: 1.4792
Epoch 1/1... Discriminator Loss: 1.1065... Generator Loss: 0.6659
Epoch 1/1... Discriminator Loss: 0.8414... Generator Loss: 1.1636
Epoch 1/1... Discriminator Loss: 0.8344... Generator Loss: 1.5650
Epoch 1/1... Discriminator Loss: 1.1405... Generator Loss: 0.5979
Epoch 1/1... Discriminator Loss: 1.0075... Generator Loss: 1.5903
Epoch 1/1... Discriminator Loss: 0.8555... Generator Loss: 1.0847
Epoch 1/1... Discriminator Loss: 0.8888... Generator Loss: 1.3325
Epoch 1/1... Discriminator Loss: 1.0542... Generator Loss: 0.6826
Epoch 1/1... Discriminator Loss: 0.6652... Generator Loss: 1.3446
Epoch 1/1... Discriminator Loss: 0.8275... Generator Loss: 1.1976
Epoch 1/1... Discriminator Loss: 1.0074... Generator Loss: 0.7282
Epoch 1/1... Discriminator Loss: 1.2301... Generator Loss: 0.4665
Epoch 1/1... Discriminator Loss: 1.5680... Generator Loss: 0.3299
Epoch 1/1... Discriminator Loss: 1.0223... Generator Loss: 2.3114
Epoch 1/1... Discriminator Loss: 0.8170... Generator Loss: 1.4038
Epoch 1/1... Discriminator Loss: 1.0335... Generator Loss: 2.5172
Epoch 1/1... Discriminator Loss: 0.7798... Generator Loss: 1.6960
Epoch 1/1... Discriminator Loss: 0.6920... Generator Loss: 2.0154
Epoch 1/1... Discriminator Loss: 0.8509... Generator Loss: 2.1719
Epoch 1/1... Discriminator Loss: 0.7743... Generator Loss: 1.1934
Epoch 1/1... Discriminator Loss: 0.8586... Generator Loss: 1.1307
Epoch 1/1... Discriminator Loss: 1.0966... Generator Loss: 1.8555
Epoch 1/1... Discriminator Loss: 1.3601... Generator Loss: 0.4065
Epoch 1/1... Discriminator Loss: 0.7112... Generator Loss: 1.3003
Epoch 1/1... Discriminator Loss: 1.2818... Generator Loss: 0.4981
Epoch 1/1... Discriminator Loss: 0.8387... Generator Loss: 1.8732
Epoch 1/1... Discriminator Loss: 0.7145... Generator Loss: 1.2823
Epoch 1/1... Discriminator Loss: 1.2232... Generator Loss: 2.1548
Epoch 1/1... Discriminator Loss: 0.9752... Generator Loss: 1.1415
Epoch 1/1... Discriminator Loss: 0.9967... Generator Loss: 0.8681
Epoch 1/1... Discriminator Loss: 0.6602... Generator Loss: 1.4384
Epoch 1/1... Discriminator Loss: 1.0550... Generator Loss: 0.6465
Epoch 1/1... Discriminator Loss: 1.1511... Generator Loss: 0.6181
Epoch 1/1... Discriminator Loss: 0.8663... Generator Loss: 1.7785
Epoch 1/1... Discriminator Loss: 0.8282... Generator Loss: 1.0015
Epoch 1/1... Discriminator Loss: 0.8972... Generator Loss: 1.0629
Epoch 1/1... Discriminator Loss: 1.0361... Generator Loss: 0.9167
Epoch 1/1... Discriminator Loss: 1.1371... Generator Loss: 0.5752
Epoch 1/1... Discriminator Loss: 1.1747... Generator Loss: 0.6071
Epoch 1/1... Discriminator Loss: 1.4138... Generator Loss: 0.3818
Epoch 1/1... Discriminator Loss: 1.1656... Generator Loss: 0.6088
Epoch 1/1... Discriminator Loss: 0.8774... Generator Loss: 1.0975
Epoch 1/1... Discriminator Loss: 0.8982... Generator Loss: 1.6643
Epoch 1/1... Discriminator Loss: 0.8270... Generator Loss: 1.0706
Epoch 1/1... Discriminator Loss: 0.9847... Generator Loss: 1.0357
Epoch 1/1... Discriminator Loss: 1.0284... Generator Loss: 0.7202
Epoch 1/1... Discriminator Loss: 1.5302... Generator Loss: 1.8005
Epoch 1/1... Discriminator Loss: 0.8331... Generator Loss: 0.9797
Epoch 1/1... Discriminator Loss: 1.1092... Generator Loss: 0.6727
Epoch 1/1... Discriminator Loss: 0.9280... Generator Loss: 0.8381
Epoch 1/1... Discriminator Loss: 1.0180... Generator Loss: 0.8344
Epoch 1/1... Discriminator Loss: 1.0243... Generator Loss: 1.2970
Epoch 1/1... Discriminator Loss: 0.9414... Generator Loss: 0.8478
Epoch 1/1... Discriminator Loss: 1.1804... Generator Loss: 0.5704
Epoch 1/1... Discriminator Loss: 0.9677... Generator Loss: 0.7748
Epoch 1/1... Discriminator Loss: 1.6711... Generator Loss: 0.3158
Epoch 1/1... Discriminator Loss: 1.2859... Generator Loss: 0.5037
Epoch 1/1... Discriminator Loss: 1.0936... Generator Loss: 0.7252
Epoch 1/1... Discriminator Loss: 0.8067... Generator Loss: 1.5938
Epoch 1/1... Discriminator Loss: 1.0527... Generator Loss: 0.6914
Epoch 1/1... Discriminator Loss: 1.3771... Generator Loss: 0.4966
Epoch 1/1... Discriminator Loss: 1.0261... Generator Loss: 1.6485

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.